How to prepare a cloud infrastructure for artificial intelligence projects

Developing an artificial intelligence solution requires more than just selecting a model or connecting an application to an API. When the project begins to process business data, run models, retrieve information, or automate actions, the infrastructure largely determines its performance, security, availability, and ability to scale.

Cloud offers resources that are particularly useful for these projects because it allows for the combination of storage, processing, data services, specialized computing capacity, and scaling mechanisms without physically building the entire infrastructure. However, having an account with a cloud provider does not mean that the architecture is ready to operate artificial intelligence in production.

Preparation must start from the use case. A periodically trained predictive model, a RAG system connected with business documentation, and an agent using corporate tools have different needs. Designing the infrastructure around those needs helps avoid unnecessary capacity while also reducing the risk of discovering limitations when the solution is already developed.

The cloud infrastructure for AI starts by defining what you want to execute

Before sizing servers, storage, or GPU capacity, it is advisable to determine what type of load the infrastructure will have to support. This decision directly affects the necessary resources and how they should be connected.

A machine learning project may need to process large datasets during training and subsequently use a much smaller infrastructure for making inferences. An application based on RAG needs continuous access to documents, embeddings, databases, and search services. A solution that consumes an LLM via an external API may require little infrastructure to run the model but a solid architecture for data, integrations, security, and observability.

AI projects must be analyzed, therefore, as complete systems. The model represents a piece of an architecture where applications, data, networks, storage, identity, monitoring, and business services are also involved.

Machine learning, RAG, LLM, and agents have different requirements

The choice of infrastructure changes considerably depending on the type of solution. Even within the same project, various loads with different behaviors can coexist.

This difference prevents a common decision: designing a technology platform first and then looking for how to adapt the use case. The architecture should take the opposite path.

Data conditions the architecture before the model

An AI solution depends on the information it can use. Therefore, preparing the infrastructure starts by identifying where the data is located, how it reaches the system, and what transformations it needs before it can be used.

Information may be distributed among databases, CRM, ERP, data warehouses, object storage, documents, SaaS applications, and internal systems. Some sources change continuously, while others may be updated through periodic processes.

Data Engineers can build pipelines that extract, transform, and distribute this information consistently. This layer is especially important when models depend on multiple sources or need to receive updated information at certain frequencies.

Storage and processing must be designed together

Not all data needs to remain in the same system. Datasets used for training can be stored in object services, while structured information can remain in databases or analytical platforms. RAG systems add search indexes or vector databases to represent and retrieve knowledge.

The architecture must determine where each type of information resides, what system constitutes the original source, and how different versions are managed. It also needs to consider retention, backups, encryption, and access policies.

As volume increases, processing becomes equally important. Transforming millions of records or generating embeddings for large document collections may require distributed processes that subsequently decrease in intensity during everyday operation.

CPU, GPU, and computing capacity must respond to the actual load

Artificial intelligence is often immediately associated with GPUs, but not all projects need to have this type of capacity permanently available.

Training certain machine learning models or running custom generative models may require specialized accelerators. In contrast, an application that consumes models via external APIs delegates much of that infrastructure to the model provider.

Machine Learning Engineers can assess training and inference needs based on the type of model, data volume, expected latency, and frequency of use.

Training and inference present different consumption patterns

Training can generate intensive loads during specific periods. Once completed, the model may remain stored until a new execution of the pipeline. Inference, on the other hand, typically responds to application requests and may need to remain continuously available.

This difference allows for the design of distinct capacity strategies. The resources used for training can be activated on demand, while the inference infrastructure can scale according to incoming traffic.

In self-hosted generative applications, model memory, concurrency, context size, and latency must also be considered. A configuration that works correctly with ten internal users may require a very different architecture when the service receives thousands of requests.

The architecture must separate development, testing, and production

AI projects experiment more than many traditional applications. Teams test models, prompts, parameters, information sources, retrieval techniques, and configurations before selecting a solution.

Maintaining that experimentation directly on production makes it difficult to control changes and increases operational risk. The infrastructure should establish differentiated environments where it is possible to develop and validate new versions before exposing them to real users.

This separation also allows for the application of permissions, spending limits, and different policies. An experimental environment can tolerate interruptions and use controlled data, while production requires greater guarantees of availability, security, and monitoring.

DevOps Engineers can automate these environments through Infrastructure as Code, deployment pipelines, and reproducible configurations, reducing differences between stages of the development cycle.

Containers and Kubernetes can provide scalability, but they are not always necessary

Containers facilitate packaging applications and their dependencies consistently. In AI projects, they can be used for inference services, APIs, pipelines, workers, and other platform components.

Kubernetes adds capabilities to orchestrate those containers, distribute loads, manage deployments, and scale services. It is especially useful when the platform contains numerous components, requires high availability, or manages frequently changing loads.

However, incorporating Kubernetes also adds operational complexity. A small project may perform better using managed services, serverless functions, or simpler container platforms.

The decision should respond to real operational needs. The sophistication of the infrastructure adds value when it solves a specific requirement for scalability, availability, or management.

Security and identity must be part of the initial design

AI systems can access business information that was previously distributed among different applications. An internal assistant can consult corporate documentation, while an agent can connect with CRM, ticket systems, databases, or productivity tools.

The infrastructure needs to determine what identity each component uses and what operations it can execute. The principles of least privilege and separation of responsibilities help limit the impact of a compromised credential or unexpected behavior.

IT Infrastructure specialists can participate in designing networks, access, and operational components that connect the solution with the existing technology ecosystem.

A RAG system must maintain the permissions of the original information

Centralizing documents within a RAG architecture should not eliminate the restrictions that existed in their source systems. If an employee does not have authorization to consult certain information, a conversational interface should not provide it simply because the document is indexed.

This requires transferring identity and permissions to the retrieval layer. The architecture must know who is making the query and filter the information that can be retrieved before sending it to the model.

In projects with sensitive information, it is also advisable to determine what data can be sent to external providers, where it is processed, and what records must be retained.

Agents require additional controls over their tools

The infrastructure of an agent incorporates an element that changes the risk: the ability to act.

Querying information and modifying a business system are different operations. An agent may need to read data from a CRM to prepare a response, but updating records, making a transaction, or sending information may require additional permissions and controls.

The architecture must define credentials, available tools, authorized operations, execution limits, and situations that require human approval. It should also maintain sufficient traceability to reconstruct what actions the system performed.

Observability for AI means monitoring more than infrastructure

CPU, memory, latency, and errors remain important metrics, but an artificial intelligence solution adds other dimensions.

An API may be technically available while the quality of responses decreases. A RAG pipeline may respond with normal latency even while retrieving irrelevant documents. A predictive model may continue to run while its results lose accuracy because the input data has changed.

Observability must combine technical metrics with signals related to the behavior of the solution. Depending on the project, it may include retrieval quality, model errors, token consumption, cost per request, tool utilization, deployed versions, and user feedback.

This information allows for distinguishing an infrastructure incident from a specific AI component problem.

MLOps connects models, infrastructure, and operation

When an organization begins to maintain multiple models or recurring training cycles, deploying them manually becomes unsustainable. MLOps introduces processes to manage experiments, versions, deployments, monitoring, and retraining.

The cloud infrastructure must facilitate this cycle. This may include model repositories, automated pipelines, reproducible environments, serving mechanisms, and controls to promote new versions between development and production.

An architecture prepared for MLOps allows knowing which model is running, with what data it was built, and which version should be restored if a problem arises.

This traceability becomes increasingly important as artificial intelligence integrates into business processes where changes need to be managed in a controlled manner.

Scalability means anticipating which component will grow

An architecture may work correctly during a proof of concept and present problems when adoption begins. The cause does not necessarily have to be found in the model.

Growth can affect different components:

• greater number of users and simultaneous requests;
• increase in the volume of documents or data processed;
• more calls to external models and APIs;
• increase in storage, embeddings, or search indexes;
• greater inference or processing capacity;
• more integrations and operations executed by agents.

Identifying which of these dimensions can grow allows for designing specific scaling mechanisms. Indiscriminately increasing the entire infrastructure often raises costs without necessarily resolving the bottleneck.

Cloud Engineers can work on capacity, networks, availability, automation, and scalability to ensure the platform responds to those changes without permanently maintaining oversized resources.

Cloud costs must be designed alongside the architecture

The elasticity of cloud allows for rapid capacity increases, but that same feature can lead to costs that are difficult to control when AI consumption grows.

GPU, inference, storage, vector databases, data transfer, and model calls can behave differently depending on usage. In generative systems, a small modification in the number of calls, context length, or number of steps executed by an agent can significantly change the cost per operation.

Therefore, the architecture should estimate from the beginning which units generate consumption and how they will evolve as utilization increases.

The cost per operation provides more information than the total bill

A solution can increase its cloud spending and still be economically efficient if the value it generates also grows. The useful metric depends on the use case: cost per processed document, answered query, prediction, active user, or automated task.

These metrics allow for comparing architectural alternatives with greater precision. They also help detect when a modification increases consumption without proportionally improving the outcome.

Economic control should be incorporated into dashboards, alerts, and capacity decisions to avoid appearing only when the monthly bill arrives.

What to review before bringing an AI project to production

A proof of concept demonstrates that an idea can work under certain conditions. Production requires checking whether the architecture can maintain that behavior when real users, changing data, errors, usage spikes, and external dependencies appear.

The review should cover data, infrastructure, security, operation, and economics. It also needs to establish who will be responsible for each component after launch. An architecture without clear ownership may function initially and degrade as changes occur that no one supervises.

At this stage, it is advisable to check that mechanisms exist to deploy new versions, recover service in the event of failures, monitor behavior, limit access, control costs, and manage changes in providers or data sources.

Preparing cloud for AI means designing a platform that can operate

The appropriate infrastructure for artificial intelligence depends on the problem the company is trying to solve. Some projects require GPUs and large data pipelines; others can be built primarily on managed services and external APIs. RAG adds recovery and permission needs, while agents incorporate tools, identities, and traceability.

Preparation consists of connecting these needs with an architecture capable of operating securely, observably, and economically sustainably. Data, computing, integration, security, MLOps, and costs must be evaluated as parts of the same system.

A well-designed infrastructure also allows for evolution. The project can start with a few users, incorporate new information sources, add models, or automate additional processes without completely rebuilding its technological foundation.

When an organization does not have all these capabilities internally, an Outsourcing IT model can incorporate specialized profiles in cloud, data, DevOps, machine learning, and artificial intelligence according to the architecture and phase of the project. The priority should be to build the technical capacity that the solution truly needs and keep it aligned with its business requirements.